import torch 

import torch.nn as nn

import torch.nn.functional as F

import torch.optim as optim

import numpy as np

import matplotlib

matplotlib.use('Agg')

import matplotlib.pyplot as plt

import matplotlib.gridspec as gridspec

import pandas as pd

import math

import sklearn.preprocessing as sk

import seaborn as sns

from sklearn import metrics

from sklearn.feature_selection import VarianceThreshold

from sklearn.model_selection import train_test_split

from torch.utils.data.sampler import WeightedRandomSampler

from sklearn.metrics import roc_auc_score

from sklearn.metrics import average_precision_score

import random

from sklearn.model_selection import StratifiedKFold

save_results_to = '/home/hnoghabi/CVClassifierResultsv9/Gemcitabine/'

max_iter = 50

torch.manual_seed(42)

# Load Mutation

GDSCE = pd.read_csv("GDSC_exprs.Gemcitabine.eb_with.PDX_exprs.Gemcitabine.tsv", sep = "\t", index_col=0, decimal = ",")

GDSCE = pd.DataFrame.transpose(GDSCE)

# Load GDSC response

GDSCR = pd.read_csv("GDSC_response.Gemcitabine.tsv", sep = "\t", index_col=0, decimal = ",")

PDXE = pd.read_csv("PDX_exprs.Gemcitabine.eb_with.GDSC_exprs.Gemcitabine.tsv", sep = "\t", index_col=0, decimal = ",")

PDXE = pd.DataFrame.transpose(PDXE)

PDXM = pd.read_csv("PDX_mutations.Gemcitabine.tsv", sep = "\t", index_col=0, decimal = ".")

PDXM = pd.DataFrame.transpose(PDXM)

PDXC = pd.read_csv("PDX_CNA.Gemcitabine.tsv", sep = "\t", index_col=0, decimal = ".")

PDXC = pd.DataFrame.transpose(PDXC)

GDSCM = pd.read_csv("GDSC_mutations.Gemcitabine.tsv", sep = "\t", index_col=0, decimal = ".")

GDSCM = pd.DataFrame.transpose(GDSCM)

GDSCC = pd.read_csv("GDSC_CNA.Gemcitabine.tsv", sep = "\t", index_col=0, decimal = ".")

GDSCC.drop_duplicates(keep='last')

PDXC = PDXC.loc[:,~PDXC.columns.duplicated()]

GDSCC = pd.DataFrame.transpose(GDSCC)

selector = VarianceThreshold(0.05)

selector.fit_transform(GDSCE)

GDSCE = GDSCE[GDSCE.columns[selector.get_support(indices=True)]]

PDXC = PDXC.fillna(0)

PDXC[PDXC != 0.0] = 1

PDXM = PDXM.fillna(0)

PDXM[PDXM != 0.0] = 1

GDSCM = GDSCM.fillna(0)

GDSCM[GDSCM != 0.0] = 1

GDSCC = GDSCC.fillna(0)

GDSCC[GDSCC != 0.0] = 1

ls = GDSCE.columns.intersection(GDSCM.columns)

ls = ls.intersection(GDSCC.columns)

ls = ls.intersection(PDXE.columns)

ls = ls.intersection(PDXM.columns)

ls = ls.intersection(PDXC.columns)

ls2 = GDSCE.index.intersection(GDSCM.index)

ls2 = ls2.intersection(GDSCC.index)

ls3 = PDXE.index.intersection(PDXM.index)

ls3 = ls3.intersection(PDXC.index)

ls = pd.unique(ls)

PDXE = PDXE.loc[ls3,ls]

PDXM = PDXM.loc[ls3,ls]

PDXC = PDXC.loc[ls3,ls]

GDSCE = GDSCE.loc[ls2,ls]

GDSCM = GDSCM.loc[ls2,ls]

GDSCC = GDSCC.loc[ls2,ls]

GDSCR.loc[GDSCR.iloc[:,0] == 'R'] = 0

GDSCR.loc[GDSCR.iloc[:,0] == 'S'] = 1

GDSCR.columns = ['targets']

GDSCR = GDSCR.loc[ls2,:]

PDXR = pd.read_csv("PDX_response.Gemcitabine.tsv", sep = "\t", index_col=0, decimal = ",")

PDXR.loc[PDXR.iloc[:,0] == 'R'] = 0

PDXR.loc[PDXR.iloc[:,0] == 'S'] = 1

Y = GDSCR['targets'].values

ls_mb_size = [32, 62]

ls_h_dim = [1024, 512, 256, 128, 64, 32, 16]

ls_z_dim = [128, 64, 32, 16]

ls_marg = [0.5, 1, 1.5, 2, 2.5]

ls_lr = [0.5, 0.1, 0.05, 0.01, 0.001, 0.005, 0.0005, 0.0001,0.00005, 0.00001]

ls_epoch = [20, 50, 10, 15, 30, 40, 60, 70, 80, 90, 100]

ls_rate = [0.3, 0.4, 0.5, 0.6, 0.7, 0.8]

ls_wd = [0.01, 0.001, 0.1, 0.0001]

skf = StratifiedKFold(n_splits=5, random_state=42)

for iters in range(max_iter):

    k = 0

    mbs = random.choice(ls_mb_size)

    hdm = random.choice(ls_h_dim)

    zdm = random.choice(ls_z_dim)

    lre = random.choice(ls_lr)

    lrm = random.choice(ls_lr)

    lrc = random.choice(ls_lr)

    lrCL = random.choice(ls_lr)

    epch = random.choice(ls_epoch)

    wd = random.choice(ls_wd)

    rate = random.choice(ls_rate)

    for train_index, test_index in skf.split(GDSCE.values, Y):

        k = k + 1

        X_trainE = GDSCE.values[train_index,:]

        X_testE =  GDSCE.values[test_index,:]

        X_trainM = GDSCM.values[train_index,:]

        X_testM = GDSCM.values[test_index,:]

        X_trainC = GDSCC.values[train_index,:]

        X_testC = GDSCM.values[test_index,:]

        y_trainE = Y[train_index]

        y_testE = Y[test_index]

        scalerGDSC = sk.StandardScaler()

        scalerGDSC.fit(X_trainE)

        X_trainE = scalerGDSC.transform(X_trainE)

        X_testE = scalerGDSC.transform(X_testE)

        X_trainM = np.nan_to_num(X_trainM)

        X_trainC = np.nan_to_num(X_trainC)

        X_testM = np.nan_to_num(X_testM)

        X_testC = np.nan_to_num(X_testC)

        TX_testE = torch.FloatTensor(X_testE)

        TX_testM = torch.FloatTensor(X_testM)

        TX_testC = torch.FloatTensor(X_testC)

        ty_testE = torch.FloatTensor(y_testE.astype(int))

        #Train

        class_sample_count = np.array([len(np.where(y_trainE==t)[0]) for t in np.unique(y_trainE)])

        weight = 1. / class_sample_count

        samples_weight = np.array([weight[t] for t in y_trainE])

        samples_weight = torch.from_numpy(samples_weight)

        sampler = WeightedRandomSampler(samples_weight.type('torch.DoubleTensor'), len(samples_weight), replacement=True)

        mb_size = mbs

        trainDataset = torch.utils.data.TensorDataset(torch.FloatTensor(X_trainE), torch.FloatTensor(X_trainM), 

                                                      torch.FloatTensor(X_trainC), torch.FloatTensor(y_trainE.astype(int)))

        trainLoader = torch.utils.data.DataLoader(dataset = trainDataset, batch_size=mb_size, shuffle=False, num_workers=1, sampler = sampler)

        n_sampE, IE_dim = X_trainE.shape

        n_sampM, IM_dim = X_trainM.shape

        n_sampC, IC_dim = X_trainC.shape

        h_dim = hdm

        Z_dim = zdm

        Z_in = h_dim + h_dim + h_dim

        lrE = lre

        lrM = lrm

        lrC = lrc

        epoch = epch

        costtr = []

        auctr = []

        costts = []

        aucts = []

        class AEE(nn.Module):

            def __init__(self):

                super(AEE, self).__init__()

                self.EnE = torch.nn.Sequential(

                    nn.Linear(IE_dim, h_dim),

                    nn.BatchNorm1d(h_dim),

                    nn.ReLU(),

                    nn.Dropout())

            def forward(self, x):

                output = self.EnE(x)

                return output

        class AEM(nn.Module):

            def __init__(self):

                super(AEM, self).__init__()

                self.EnM = torch.nn.Sequential(

                    nn.Linear(IM_dim, h_dim),

                    nn.BatchNorm1d(h_dim),

                    nn.ReLU(),

                    nn.Dropout())

            def forward(self, x):

                output = self.EnM(x)

                return output    

        class AEC(nn.Module):

            def __init__(self):

                super(AEC, self).__init__()

                self.EnC = torch.nn.Sequential(

                    nn.Linear(IM_dim, h_dim),

                    nn.BatchNorm1d(h_dim),

                    nn.ReLU(),

                    nn.Dropout())

            def forward(self, x):

                output = self.EnC(x)

                return output      

        class Classifier(nn.Module):

            def __init__(self):

                super(Classifier, self).__init__()

                self.FC = torch.nn.Sequential(

                    nn.Linear(Z_in, Z_dim),

                    nn.ReLU(),

                    nn.Dropout(rate),

                    nn.Linear(Z_dim, 1),

                    nn.Dropout(rate),

                    nn.Sigmoid())

            def forward(self, x):

                return self.FC(x)

        torch.cuda.manual_seed_all(42)

        AutoencoderE = AEE()

        AutoencoderM = AEM()

        AutoencoderC = AEC()

        solverE = optim.Adagrad(AutoencoderE.parameters(), lr=lrE)

        solverM = optim.Adagrad(AutoencoderM.parameters(), lr=lrM)

        solverC = optim.Adagrad(AutoencoderC.parameters(), lr=lrC)

        Clas = Classifier()

        SolverClass = optim.Adagrad(Clas.parameters(), lr=lrCL, weight_decay = wd)

        C_loss = torch.nn.BCELoss()

        for it in range(epoch):

            epoch_cost4 = 0

            epoch_cost3 = []

            num_minibatches = int(n_sampE / mb_size) 

            for i, (dataE, dataM, dataC, target) in enumerate(trainLoader):

                flag = 0

                AutoencoderE.train()

                AutoencoderM.train()

                AutoencoderC.train()

                Clas.train()

                if torch.mean(target)!=0. and torch.mean(target)!=1.:                      

                    ZEX = AutoencoderE(dataE)

                    ZMX = AutoencoderM(dataM)

                    ZCX = AutoencoderC(dataC)

                    ZT = torch.cat((ZEX, ZMX, ZCX), 1)

                    ZT = F.normalize(ZT, p=2, dim=0)

                    Pred = Clas(ZT)

                    loss = C_loss(Pred,target.view(-1,1))   

                    y_true = target.view(-1,1)

                    y_pred = Pred

                    AUC = roc_auc_score(y_true.detach().numpy(),y_pred.detach().numpy()) 

                    solverE.zero_grad()

                    solverM.zero_grad()

                    solverC.zero_grad()

                    SolverClass.zero_grad()

                    loss.backward()

                    solverE.step()

                    solverM.step()

                    solverC.step()

                    SolverClass.step()

                    epoch_cost4 = epoch_cost4 + (loss / num_minibatches)

                    epoch_cost3.append(AUC)

                    flag = 1

            if flag == 1:

                costtr.append(torch.mean(epoch_cost4))

                auctr.append(np.mean(epoch_cost3))

                print('Iter-{}; Total loss: {:.4}'.format(it, loss))

            with torch.no_grad():

                AutoencoderE.eval()

                AutoencoderM.eval()

                AutoencoderC.eval()

                Clas.eval()

                ZET = AutoencoderE(TX_testE)

                ZMT = AutoencoderM(TX_testM)

                ZCT = AutoencoderC(TX_testC)

                ZTT = torch.cat((ZET, ZMT, ZCT), 1)

                ZTT = F.normalize(ZTT, p=2, dim=0)

                PredT = Clas(ZTT)

                lossT = C_loss(PredT,ty_testE.view(-1,1))         

                y_truet = ty_testE.view(-1,1)

                y_predt = PredT

                AUCt = roc_auc_score(y_truet.detach().numpy(),y_predt.detach().numpy())

                costts.append(lossT)

                aucts.append(AUCt)

        plt.plot(np.squeeze(costtr), '-r',np.squeeze(costts), '-b')

        plt.ylabel('Total cost')

        plt.xlabel('iterations (per tens)')

        title = 'Cost Gemcitabine iter = {}, fold = {}, mb_size = {},  hz_dim[1,2] = ({},{}), lr[E,M,C] = ({}, {}, {}), epoch = {}, wd = {}, lrCL = {}, rate4 = {}'.\

                      format(iters, k, mbs, hdm, zdm , lre, lrm, lrc, epch, wd, lrCL, rate)

        plt.suptitle(title)

        plt.savefig(save_results_to + title + '.png', dpi = 150)

        plt.close()

        plt.plot(np.squeeze(auctr), '-r',np.squeeze(aucts), '-b')

        plt.ylabel('AUC')

        plt.xlabel('iterations (per tens)')

        title = 'AUC Gemcitabine iter = {}, fold = {}, mb_size = {},  hz_dim[1,2] = ({},{}), lr[E,M,C] = ({}, {}, {}), epoch = {}, wd = {}, lrCL = {}, rate4 = {}'.\

                      format(iters, k, mbs, hdm, zdm , lre, lrm, lrc, epch, wd, lrCL, rate)        

        plt.suptitle(title)

        plt.savefig(save_results_to + title + '.png', dpi = 150)

        plt.close()